What is data analytics, and why is it important for organisations to utilise?
Data analytics is the process of collecting, cleansing, transforming and analysing an organisation’s information to identify trends and extract meaningful insights to solve problems.
The main benefit for procurement teams that adopt analytics is that they’re equipped to make faster, more proactive and effective decisions. Spend analysis and other advanced statistical analyses eliminate the guesswork and reactivity common with spreadsheets and other manual approaches and drive greater efficiency and value.
As procurement continues to play a central role in organisational success, adopting analytics is critical for improving operations, meeting and achieving key performance indicators, reducing staff burnout, gaining valuable market intelligence and protecting the bottom line.
How can organisations use procurement analytics to benefit their operations?
Teams can leverage data analytics to tangibly improve performance across all procurement activities - identifying new savings opportunities, getting a consolidated view of spend, understanding the right time for contract re-negotiations, and which suppliers to tap when prioritising and segmenting suppliers, assessing and addressing supply chain risk and more.
Procurement can ultimately create a more comprehensive sourcing process that invites more suppliers to the table and gets even more granular about cost drivers and other criteria.
"The main benefit for procurement teams that adopt analytics is that they’re equipped to make faster, more proactive and effective decisions"
Procurement analytics can provide critical insight for spend management, category management, supplier contracts and negotiations, strategic sourcing, spend forecasting and more. Unilever, for example, used actionable insight from spend analysis to optimise spending, sourcing, and contract negotiations for an especially unpredictable industry such as transport and logistics.
Whether a team needs to figure out ways to retain cash, further diversify its supply base, or deliver value on sustainability, innovation or diversity initiatives, analytics can help procurement deliver on organisational needs.
How is data analytics used in supply chain and procurement?
Data analytics encompasses descriptive, diagnostic, predictive and prescriptive data.
Descriptive shows what’s happened in the past, while diagnostic analytics surface answers to ‘why’ those previous events happened.
This clear view into procurement operations and trends lays the groundwork for predictive analytics, which forecasts future events, and prescriptive analytics, which recommends the best actions for teams to take based on those predictions.
Teams can leverage all four types of analytics to gain visibility across the supply chain and identify optimisation and value generating opportunities.
Take on-time delivery (OTD) as an example. Predictive analytics are identifying the probability of whether an order will be delivered on time even before its placed, based on previous events. Combined with recommendation engines that suggest improvement actions, the analytics enable teams to proactively mitigate risk of late deliveries, such as through spreading an order over a second or third source of supply.
Advanced analytics is a research and development focus for JAGGAER, and we expect procurement’s ability to leverage AI to become even stronger and more impactful.
- P&SC London templateSupply Chain Risk Management
- Dynamic data key to unlocking supplier value - TealBook CEOProcurement
- Third-party supply risk 'key to survival' - Refinitiv reportSupply Chain Risk Management
- IBM supply chain head's digital transformation insightSupply Chain Risk Management